As is well known, government regulations require vehicles equipped with internal combustion engines to have emission monitoring systems conventionally known as OBD (On-board Diagnostic Systems) to advise the operator of the vehicle when the gaseous pollutants or emissions produced by such vehicles exceed government regulatory standards. Government regulatory standards set emission threshold levels which the vehicle cannot exceed when operated pursuant to a specified driving cycle such as that set forth in a FTP (Federal Test Procedure). The FTP requires the vehicle be operated at various acceleration/deceleration modes as well as at steady state or constant velocity at various specified speeds.
One of the principal components of the vehicle's emission system is the catalytic converter, typically a TWC (Three Way Catalyst--NO.sub.x, hydrocarbons and oxides, i.e., CO). TWCs store oxygen when the engine operates lean and release stored oxygen when the engine operates rich to combust gaseous pollutants such as hydrocarbons or carbon monoxide. As the catalyst ages, its ability to store oxygen diminishes and thus the efficiency of the catalytic converter decreases. Conventional systems in use today monitor the ability of TWCs to store oxygen to determine failure of the catalyst. Typically an EGO (exhaust gas oxygen sensor) is placed upstream of the TWC and an oxygen sensor is placed either within or downstream of the TWC to sense the oxygen content in the exhaust gas. The signals are adjusted for the time it takes the exhaust gas to travel from the precatalytic converter sensor to the postcatalytic converter oxygen sensor. The adjusted signals are then compared to ascertain the storage capacity of the TWC when the engine is in a lean or stoichiometric mode. The principal disadvantage of this method is simply that the oxygen storage capacity of the TWC has been demonstrated to poorly correlate with hydrocarbon conversion efficiencies. See J. S. Hepburn and H. S. Gandhi "The Relationship Between Catalyst Hydrocarbon Conversion Efficiency and Oxygen Storage Capacity", SAE paper 920831, 1992 and G. B. Fischer, J. R. Theis, M. V. Casarella and S. T. Mahan "The Roll of Ceria in Automotive Exhaust Catalysis and OBD-II Catalyst Monitoring", SAE paper 931034, 1993. Another significant disadvantage of current monitoring systems is that for such methods to be applied to low emission vehicles and ultra-low emission vehicles, it will be necessary to monitor increasingly smaller portions of the TWC leading to less reliable correlations to total TWC performance. Finally, any system which attempts to evaluate the efficiency of the catalytic converter by ascertaining the gas composition of an exhaust stream before and after the TWC is inherently flawed because i) the speed of the gas has to be precisely determined even though the gas stream passes through a tortuous path within the converter conducive to producing uneven flow for various gas stream slips and ii) the gaseous reactions within the TWC are fundamentally kinetic in nature and vary in a complex manner depending on the speed and particular composition of the exhaust gas at any given instant.
Today's automotive vehicles are equipped with engine microprocessors or ECMs (engine control modules) that are sophisticated, high powered devices capable of processing input from any number of sensors depicting operating conditions of the vehicle and rapidly issuing engine control signals in response thereto. It is well known to program the ECM to perform on-board monitoring of the emissions system. U.S. Pat. No. 5,490,064 to Minowa illustrates such a control unit which includes in its functions an on-board self diagnostic emissions monitoring process using conventional pre and postcatalytic O.sub.2 sensors, digital filtering and exhaust gas speed to correlate the sensor readings to one another to determine failure of the catalytic converter. U.S. Pat. No. 5,431,011 to Casarella et al. likewise illustrates precatalytic and postcatalytic converter O.sub.2 sensors whose signals are processed by the CPU in the ECM along with other vehicle operational signals. In Casarella, a two-stage analyzing technique is utilized. Filtered signals are collected in a first stage and analyzed. If the first stage analysis indicates a failure, then a more thorough or rigorous second stage scrutiny of a number of signals which can effect performance of the catalytic converter is conducted before indicating failure of the converter. Despite the sophistication employed in the computer program and the ECM, the aforementioned system is inherently flawed because of the defects in the sampling system discussed above.
Recently, artificial intelligence approaches have been used to avoid reliance on algorithms to calculate the converter activity based upon the laws of physics and/or chemistry. U.S. Pat. Nos. 5,539,638 to Keeler et al. and 5,625,750 to Puskorius et al. illustrate use of sophisticated computerized neural networks utilizing training programs to predict failure of the catalytic converter. In Keeler, the parameters which control the operation of the vehicle's engine such as temperature, back pressure, valve position, etc. are detected by various sensors employed on the vehicle including the conventional EGO sensor upstream of the catalytic converter and are trained in the sense that the operating conditions vis-a-vis the vehicles ECM are correlated to an emissions level produced by the vehicle's engine for the sensed operating conditions of the engine. The signals are stored on a time basis and factored and compared to a threshold value indicative of a regulatory standard at which an emissions failure is deemed to have occurred. In Keeler, training of the neural network is accomplished by simply inserting whatever sensor probe is used by the government regulatory agency in the vehicle's exhaust pipe to generate emissions data correlated to the sensed parameter. Puskorius discloses a more sophisticated neural network and additionally uses an EGO sensor position rearwardly of the catalytic converter to provide two neural artificial intelligence networks with feedback. One network establishes emissions before the catalytic converter and the second network establishes emissions after the catalytic converter. Training data for the networks is provided from a data bank of a large number of similar vehicles with data accumulated over the expected vehicle life. In operation, then, a trained neural network provides an efficiency ratio accumulated over time which is compared against stored data to predict when failure of the catalytic converter will occur. While an artificial intelligence computerized network may appear theoretically sound, in practice, it is only as good as the training data which cannot be precisely correlated to that one specific vehicle which is being sampled. The assumption is made that any specific vehicle will produce emissions at the level of that produced by the vehicle(s) from which the training data was assimilated.
The prior art has also recognized that sensors other than oxygen sensors can be employed to measure gaseous pollutants in the exhaust stream of an internal combustion engine. U.S. Pat. Nos. 5,451,371 to Zanini-Fisher et al. and 5,265,417 to Visser et al. illustrate hydrocarbon sensors of the calorimeter and micro-calorimeter type which are somewhat similar to pellistor sensors and which have been specifically developed for monitoring hydrocarbon emissions. In U.S. Pat. No. 5,476,001 to Hoetzel et al., an exhaust gas sensor is disclosed which is capable of determining pollutant components independently of the oxygen partial pressure of the exhaust gas. Still another type of sensor is shown in U.S. Pat. No. 5,610,844 to Maus et al. which illustrates use of temperature differentials, from temperature measurements within the catalytic converter, to determine the exhaust gas composition. It is well known however that sensor life directly corresponds to the temperature of the exhaust gases. Placing sensors within the catalytic converter invariably leads to short life expectancy of the sensors.
U.S. Pat. Nos. 5,177,464 to Hamburg, 5,265,417 to Visser et al. and 5,408,215 to Hamburg, all disclose the use of a hydrocarbon sensor to determine the efficiency of the TWC by sequentially tapping portions of the exhaust gas stream, upstream and downstream of the TWC, and comparing the ratios of sensed hydrocarbons to determine an efficiency ratio which in turn is stored in a register and sampled or averaged and compared against threshold values to determine or predict catalytic failure. This prior art, which discloses the use of hydrocarbon sensors (and NOx sensors) instead of EGO sensor, is similar to conventional systems and methods described above which monitor the efficiency of the catalytic converter by precatalytic and post catalytic converter measurements so that when the catalytic converter ages and its efficiency diminishes, the operator can be warned that the emission system of the vehicle has, or will shortly fail and the vehicle must be serviced. It should be recognized that the prior art systems discussed above do not really care what emissions are produced by the vehicle. So long as the upstream and downstream measurements can record catalytic activity in a consistent manner, efficiency ratios can be promulgated and arbitrarily assigned a pass fail ratio. When the efficiency ratio reaches a preset value, it is expected that the vehicle will fail the FTP. It should also be noted that the prior art approach caters to OBD regulations in that the monitoring system is to indicate what part of the emissions control systems on the vehicle has failed.
U.S. Pat. No. 5,444,974 to Beck et al. and the FIG. 6 embodiment of the Hamburg references disclose a fundamentally different approach than that discussed above. In Beck, an especially developed calorimetric sensor is used downstream of the TWC to sense hydrocarbon emissions of the vehicle. Tests established that the especially developed calorimetric sensor correlates to emissions when the vehicle is operated at certain conditions, including constant speed and at stoichiometric or lean engine conditions. Beck thus filters the signals developed by his calorimetric sensor so that only the signals developed when the vehicle is at specified operating conditions are collected. Thus, an EGO sensor upstream of the TWC is used to accept/reject signals for storage. The stored signals are then built into a histogram which is compared to a threshold histogram for activating the vehicle's fault indicating light. The FTP, however, requires the vehicle to meet varying speed requirements irrespective of whether certain emissions of the vehicle satisfy preset conditions. In Hamburg's alternative embodiment, the HC sensor simply senses the post catalyst exhaust stream content and compares the sensed value to a stored reference value for that engine speed and load. This concept is fundamentally sound and the present invention may be viewed as an extension or further refinement thereof. In the alternative embodiment of Hamburg there is no attempt to ascertain where the failure in the vehicle's emission system occurred nor is there any attempt to correlate the readings to duplicate an FTP.